Incorporating prior knowledge with simulation data to estimate PSF multipliers using Bayesian logistic regression

Recently, several kinds of databases have been constructed and analyzed to support human reliability analyses. Based on these, some researchers have attempted to model the quantitative relations between performance shaping factors and human error probability. However, the limitations of the traditional regression technique and simulation data employed have come to light. To tackle these issues regarding the traditional statistical analysis, this study proposes an analysis based on the Bayesian logistic regression method that incorporates empirical data with prior knowledge. This method was applied to four different prior knowledge sets and empirical data collected via the Human Reliability data Extraction (HuREX) framework. The mean and credible interval from the obtained posterior distributions were compared with previous research. From the application, we found that the suggested approach is useful in consolidating various data sources to estimate the multipliers of performance shaping factors on error probabilities, producing results robust to the data characteristics, and providing the quantitative uncertainties of the estimation. It is also confirmed that selecting an appropriate prior knowledge and collecting abundant and correct empirical data are important for producing meaningful insights for PSF impacts.